The present invention generally relates to machine learning, and more specifically, to hybrid acceleration in a processing environment.
In machine learning and deep learning environments, acceleration is utilized for building and training models quickly when dealing with large volumes of data. Machine learning is essentially pattern recognition and machine learning models or algorithms can learn from large amounts of training data to infer predictions on data. These models allow for results that are reliable and repeatable. Also, these models are sometimes utilized to discover hidden insights into data through learning from the historical relationship and/or trends in data. There is much focus on being able to develop machine learning models that are produced faster and, if necessary, re-trained faster for deployment in data analytics.